Defects design in 2D materials via high-throughput calculation and machine learning

POSTER

Abstract

Employing high throughput DFT calculations, we study the crystal structure, stability, and electronic structures of defects in 2D materials such as hexagonal boron nitride and transition metal dichalcogenides. The interaction of defects was evaluated by comparing the formation energies of defect complexes and individual defects. A mean-field theory model was constructed to understand the interaction dynamics of defects. Machine learning models were trained to predict the stability and electronic properties of defects.

*This research is supported by the Ministry of Education, Singapore, under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials (R-730-000-001-135 (I-FIM MOE grant)). The computational work for this article was performed on resources at the National Supercomputing Centre and Centre for Advanced 2D Materials, Singapore (https://www.nscc.sg).

Presenters

  • Pengru Huang

    • Institute for Functional Intelligent Materials, National University of Singapore

Authors

  • Pengru Huang

    • Institute for Functional Intelligent Materials, National University of Singapore

  • Miguel Dias Costa

    • National University of Singapore

  • Ruslan Lukin

    • Innopolis University

  • Nikita Kazeev

    • HSE University

  • Andrey Ustyuzhanin

    • HSE University

  • Alexander Tormasov

    • Innopolis university

  • Antonio Castro Neto

    • National University of Singapore

  • Kostya Novoselov

    • Institute for Functional Intelligent Materials, National University of Singapore
    • National University of Singapore